Decoding Multiple Myeloma: Single-Cell Insights into Tumor Heterogeneity, Immune Dynamics, and Disease Progression

Zhenzhen Zhao^1*Zhijie Zhao²Zhiheng Lin³Lu Fan⁴Xiahou Zhikai^5*Yujiang Dong^6*Weiying Bao^7*

• ¹First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
• ²Department of Plastic and Reconstructive Surgery, Shanghai Ninth People’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
• ³Department of Gynecology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
• ⁴Department of Hematology, Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
• ⁵China Institute of Sport and Health Science, Beijing Sport University, Beijing, China
• ⁶Department of Cardiology, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China
• ⁷Department of Hematology, Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine,, Shanghai, China

Background: Multiple myeloma (MM) is a biologically heterogeneous malignancy of clonal plasma cells, often progressing from MGUS or smoldering MM. It causes anemia, bone lesions, and immune dysfunction due to abnormal plasma cell expansion in the bone marrow. Neuroinflammatory and neurotrophic factors may influence MM progression by affecting immune cells and the bone marrow niche. Growing evidence points to a role for neuroimmune regulation in tumor immunity. Despite therapeutic progress, disease heterogeneity and resistance highlight the need for new strategies targeting the tumor microenvironment and neuroimmune axis.Methods: This investigation exploited single-cell RNA sequencing (scRNA-seq) to analyze MM and high-risk smoldering multiple myeloma (SMMh) samples, identifying 17 cell clusters and 11 distinct cell types. We examined their transcriptional signatures, stemness, proliferative properties, and metabolic pathways, with particular attention to neuroimmune interactions in the tumor microenvironment. Using trajectory inference tools such as CytoTRACE, Monocle2, and Slingshot, we traced the differentiation paths of MM cell subpopulations and identified key signaling pathways that may influence immune responses and tumor progression.Results: The analysis identified four distinct subpopulations of myeloma cells, with the C0 IGLC3+ myeloma cells representing the least differentiated and most proliferative subset. These cells played a critical role in MM progression and may contribute to immune evasion mechanisms. Additionally, receptor-ligand interactions within the tumor microenvironment were identified, which may be influenced by neuroinflammatory and neurotrophic factors. These findings suggest that the nervous system and immune modulation significantly affect tumor biology, highlighting potential therapeutic targets that could be exploited to overcome resistance to conventional therapies.Conclusion: This single-cell analysis provided new insights into the cellular diversity and differentiation trajectories in MM, offering a deeper understanding of the complex neuroimmune interactions that drive tumor progression and resistance. By incorporating the role of neuroinflammation and immune modulation, our study suggested novel therapeutic strategies targeting the neuroimmune axis in oncology, ultimately contributing to the development of more effective, personalized treatment approaches for MM.